Course syllabus

DAT566 / DIT408 -- Introduction to Data Science and AI

LP3 VT26 (7.5 HP)

Course offered by the Department of Computer Science and Engineering

 

Contact

Examiner:	Matti Karppa	karppa@chalmers.se
Lecturer:	Oana Geman	geman@chalmers.se
Lecturer:	Sandro Stucki	sandro.stucki@chalmers.se
Lecturer:	Bastiaan Bruinsma	sebastianus.bruinsma@chalmers.se
Lecturer:	Shuai Wang	shuaiwa@chalmers.se
Lecturer:	Shirin Tavara	tavara@chalmers.se
Lecturer:	Farzaneh Jalalypour	farjal@chalmers.se
Teaching Assistant:	Andrea Silvi	andrea.silvi@chalmers.se
Teaching Assistant:	Télio Cropsal	telio.cropsal@gu.se
Teaching Assistant:	Amer Mustajbasic	amer.mustajbasic@gu.se
Teaching Assistant:	Arsham Khoee	arsham.khoee@chalmers.se
Teaching Assistant:	Muhammad Danish Waseem	muhammad.danish.waseem@gu.se
Teaching Assistant:	Georg Kyhn	merila@chalmers.se
Teaching Assistant:	Miaowen Dong	miaowen@chalmers.se
Teaching Assistant:	Jonathan Dahlqvist	jondahl@student.chalmers.se
Teaching Assistant:	Sackarias Lunman	lunman@chalmers.se

 

Course Representatives:

Name	e-mail
Name	e-mail
Name	e-mail

 

Schedule

The course includes 2 in-person lectures each week, as well as 3 laboratory sessions. The exception is Week 8, when there is only a lecture on Friday and two laboratory sessions on the Thursday and Friday.

The schedule for lectures and sessions is available here: TimeEdit

 

Lectures

Week	Weekday	Date	Start time	End time	Room	Lecturer/responsible
v 4	Tuesday	2026-01-20	13:15	15:00	GD-Salen	MK
v 4	Friday	2026-01-23	13:15	15:00	GD-Salen	MK
v 5	Tuesday	2026-01-27	13:15	15:00	GD-Salen	FJ
v 5	Friday	2026-01-30	13:15	15:00	GD-Salen	FJ
v 6	Tuesday	2026-02-03	13:15	15:00	GD-Salen	SW
v 6	Friday	2026-02-06	13:15	15:00	GD-Salen	SW
v 7	Tuesday	2026-02-10	13:15	15:00	GD-Salen	ST
v 7	Friday	2026-02-13	13:15	15:00	GD-Salen	ST
v 8	Friday	2026-02-20	13:15	15:00	GD-Salen	OG
v 9	Tuesday	2026-02-24	13:15	15:00	GD-Salen	SS
v 9	Friday	2026-02-27	13:15	15:00	GD-Salen	SS
v 10	Tuesday	2026-03-03	13:15	15:00	GD-Salen	SS
v 10	Friday	2026-03-06	13:15	15:00	GD-Salen	SS
v 11	Tuesday	2026-03-10	13:15	15:00	GD-Salen	BB
v 11	Friday	2026-03-13	13:15	15:00	GD-Salen	BB

 

Lab Sessions

Lab sessions in various computer rooms on campus. Here, TA's will be able to help you with the workbooks.

Week	Day	Date	Start	End	Rooms
v 4	Tuesday	2026-01-20	15:15	17:00	ES61, ES62, ES63
v 4	Thursday	2026-01-22	13:15	15:00	HB105, HB110
v 4	Friday	2026-01-23	10:00	11:45	ES61, ES62, ES63
v 5	Tuesday	2026-01-27	15:15	17:00	ES61, ES62, ES63
v 5	Thursday	2026-01-29	13:15	15:00	HB105, HB110
v 5	Friday	2026-01-30	10:00	11:45	ES61, ES62, ES63

v 6	Tuesday	2026-02-03	15:15	17:00	ES61, ES62, ES63
v 6	Thursday	2026-02-05	13:15	15:00	HB105, HB110
v 6	Friday	2026-02-06	10:00	11:45	ES61, ES62, ES63

v 7	Tuesday	2026-02-10	15:15	17:00	ES61, ES62, ES63
v 7	Thursday	2026-02-12	13:15	15:00	HB105, HB110
v 7	Friday	2026-02-13	10:00	11:45	ES61, ES62, ES63

v 8	Thursday	2026-02-19	13:15	15:00	HB105, HB110
v 8	Friday	2026-02-20	10:00	11:45	ES61, ES62, ES63
v 9	Tuesday	2026-02-24	15:15	17:00	ES61, ES62, ES63
v 9	Thursday	2026-02-26	13:15	15:00	HB105, HB110
v 9	Friday	2026-02-27	10:00	11:45	ES61, ES62, ES63
v 10	Tuesday	2026-03-03	15:15	17:00	ES61, ES62, ES63
v 10	Thursday	2026-03-05	13:15	15:00	HB105, HB110
v 10	Friday	2026-03-06	10:00	11:45	ES61, ES62, ES63
v 11	Tuesday	2026-03-10	15:15	17:00	ES61, ES62, ES63
v 11	Thursday	2026-03-12	13:15	15:00	HB105, HB110
v 11	Friday	2026-03-13	10:00	11:45	ES61, ES62, ES63

Literature

Skiena, Steven S. (2017). The Data Science Design Manual. Springer.

Available through Chalmers Network and Library at https://link.springer.com/book/10.1007/978-3-319-55444-0 (Link opens an external website) 

Additional literature can be found in their respective modules.

Minerva

The supported environment for this course is the Minerva cluster. Please see instructions and apply for access here: https://git.chalmers.se/karppa/minerva 

Design

There is no mandatory attendance at either the Lectures or Laboratory sessions. However, you are unlikely to pass the course and the assignments if you do not attend.

Laboratory sessions consist of independent work on the assignments and there will be course staff available to help. The teaching assistants will primarily answer questions related to the assignment of the week, and you are assumed to have attended the lectures and done the weekly readings. General question about how to install Python etc will only be answered if there is time. There are plenty of resources for this online, see material for weeks 1-2 of the course.

Lectures are in-person only. There is required reading attached for each lecture which you are expected to read before the lecture. Lectures will assume you have read the literature and will not repeat it.

Slack

You can join the course slack using this link: https://join.slack.com/t/idsai/shared_invite/zt-3nmme19v5-IrpHjMEsU_pJFiBYso~DJQ

The course examiner and other staff are around the help answer your questions.

Assignments

• All assignments are done in groups of two students.
• The initial deadline is one week AFTER initial release. Note the exceptions due to public holidays. It is your responsibility to keep yourselves informed about each individual deadline!
• You will receive feedback and grading ONE WEEK after the initial deadline.
• If you did not pass, you can resubmit. The resubmission deadline is TWO WEEKS after the initial deadline. After this, the assignment is closed.
• Resubmissions do not receive feedback, only grading.
• Late submissions are considered resubmissions (and thus receive no feedback).
• If you fail the resubmission, you will need to complete that assignment again in a later instance of the course (e.g. LP1).
• All deadlines are HARD.
• Extensions are only given for valid reasons such as illness, serious family issues etc -- not for holiday trips.
• To request an extension, you are required to email the Examiner BEFORE the initial deadline, with the Lecturer for that week in cc. 
• All assignments must be submitted through Canvas.

 

Learning Objectives and Syllabus

On successful completion of the course the student will be able to:

Knowledge and understanding

• describe fundamental types of problems and main approaches in data science and AI
• give examples of data science and AI applications from different contexts
• give examples of how stochastic models and machine learning (ML) are applied in data science and AI
• explain basic concepts in classical AI, and the relationship between logical and data driven, ML-based approaches within AI.
• briefly explain the historical development of AI, what is possible today and discuss possible future development.

Skills and abilities

• use appropriate programming libraries and techniques to implement basic transformations, visualizations and analyses of example data
• identify appropriate types of analysis problems for some concrete data science applications
• implement some types of stochastic models and apply them in data science and AI applications
• implement and/or use AI-tools for search, planning and problem solving
• apply simple machine learning methods implemented in a standard library

Judgement and approach

• justify which type of statistical method is applicable for the most common types of experiments in data science applications
• discuss advantages and drawbacks of different types of approaches and models within data science and AI.
• reflect on inherent limitations of data science methods and how the misuse of statistical techniques can lead to dubious conclusions
• critically analyze and discuss data science and AI applications with respect to ethics, privacy and societal impact
• show a reflective attitude in all learning

For more information, see also the syllabus:

https://www.chalmers.se/en/education/your-studies/find-course-and-programme-syllabi/course-syllabus/DAT566/DIT408/?acYear=2026/2027

If the course is a joint course (Chalmers and Göteborgs Universitet) you should link to both syllabus (Chalmers and Göteborgs Universitet).